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Record W2173555751 · doi:10.1080/07055900.2015.1106401

Estimating Sea-Level Allowances for Atlantic Canada using the Fifth Assessment Report of the IPCC

2015· article· en· W2173555751 on OpenAlexafffundvenueabout
Li Zhai, B. J. W. Greenan, John Hunter, T. S. James, Guoqi Han, Phillip MacAulay, J. A. Henton

Bibliographic record

VenueATMOSPHERE-OCEAN · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsUniversity of VictoriaBedford Institute of OceanographyGeological Survey of CanadaNatural Resources CanadaFisheries and Oceans Canada
FundersNatural Resources CanadaUniversität Hamburg
KeywordsEnvironmental scienceClimatologyClimate changeOceanographyGeographyGeology

Abstract

fetched live from OpenAlex

Sea-level allowances at 22 tide-gauge sites along the east coast of Canada are determined based on projections of regional sea-level rise for the Representative Concentration Pathway 8.5 (RCP8.5) from the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR5) and on the statistics of historical tides and storm surges (storm tides). The allowances, which may be used for coastal infrastructure planning, increase with time during the twenty-first century through a combination of mean sea-level rise and the increased uncertainty of future projections with time. The allowances show significant spatial variation, mainly a consequence of strong regionally varying relative sea-level change as a result of glacial isostatic adjustment (GIA). A methodology is described for replacement of the GIA component of the AR5 projection with global positioning system (GPS) measurements of vertical crustal motion; this significantly decreases allowances in regions where the uncertainty of the GIA models is large. For RCP8.5 with GPS data incorporated and for the 1995–2100 period, the sea-level allowances range from about 0.5 m along the north shore of the Gulf of St. Lawrence to more than 1 m along the coast of Nova Scotia and southern Newfoundland.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.406

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.059
GPT teacher head0.265
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2015
Admission routes4
Has abstractyes

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